A multi-model approach to solve industry-specific machine learning problems

Abstract

Machine Learning (ML) applications across industries face unique challenges due to the diversity of data types, sources, and domain-specific requirements. This paper presents a comprehensive multi-model approach for solving industry-specific ML problems, leveraging modern frameworks and libraries for data prepa3.ration, processing, and model utilization. The proposed pipeline incorporates robust methodologies for data handling, embedding generation, and prompt answering to optimize decision-making processes in various sectors. Detailed evaluation metrics, case studies, and future enhancement opportunities are explored to validate the framework's effectiveness and adaptability in real-world scenarios.